Statistical approach and feedback for genetic, epidemiological or clinical data.

2022 Fiscal Year Final Research Report

• Project/Area Number: 18K11189
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 一般
• Review Section: Basic Section 60030:Statistical science-related
• Research Institution: Yokohama City University (2019-2022); Tokyo Medical and Dental University (2018)
• Principal Investigator: Tomita Makoto 横浜市立大学, データサイエンス学部, 教授 (20399025)
• Project Period (FY): 2018-04-01 – 2023-03-31
• Keywords: ゲノムデータ解析 / 空間集積性 / データサイエンス学部 / ヘルスデータサイエンス研究

Outline of Final Research Achievements

In recent years, studies of genetic diversity analysis have moved toward larger and larger data sets, and studies of epidemiology and mental health data have also begun to deal with large, spatially sampled, and vast amounts of information. The former is not simply a matter of statistical analysis, but has a unique genetic approach in this field, while the latter is also limited in its applicability due to computational expansion and practical limitations in computing time when considering spatial agglomeration.
Regarding the status of the use of data science, he gave an invited oral presentation at ECDA 2022 (Naples, Italy), and his paper summarized as an overview of genomic data analysis was accepted by WIREs Computational Statistics and will be published in 2023.

Free Research Field

生物統計学

Academic Significance and Societal Importance of the Research Achievements

データサイエンスを活用した状況については、ECDA2022(Naples, Italy)で招聘の口頭発表を行い、ゲノムデータ解析の統括としてまとめた論文がWIREs Computational Statisticsに採択され、2023年に掲載される運びとなった。前者は、本研究より大きな枠組みとなるデータサイエンスやヘルスデータサイエンスの潮流や最新情報を含むものであり、後者はゲノムデータ解析にまつわるデータ検証やデータ構造、解析アプローチなどを総合的に紹介しており、分野に専門・非専門を問わず有用な知識と情報を寄与できるものとなった。